class_docstr: Factory class to create the different tensorflow graphs for ner_dl, generic_classifier, assertion_dl, relation_extraction annotators in spark-nlp healthcare
docstr: Method that return the params allowed for the tf model.This method return the params with the description for every param.
  
  Parameters
  ----------
  model_name: str
      The name of the tf model name.Model availables ner_dl,generic_classifier,assertion_dl and relation_extraction
  
  Examples
  --------
  >>> from sparknlp.training import tf_graph
  >>> tf_graph.print_model_params("ner_dl")
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code: def print_model_params(model_name):
      """
      Method that return the params allowed for the tf model.This method return the params with the description for every param.
  
      Parameters
      ----------
      model_name: str
          The name of the tf model name.Model availables ner_dl,generic_classifier,assertion_dl and relation_extraction
  
      Examples
      --------
      >>> from sparknlp.training import tf_graph
      >>> tf_graph.print_model_params("ner_dl")
      """
      if model_name not in TFGraphBuilderFactory.get_models():
          raise Exception(f"Model {model_name} not supported.")
  
      model = TFGraphBuilderFactory.__model_builders[model_name]({})
      model_params = model.get_model_build_params()
      model_params_descr = model.get_model_build_param_explanations()
  
      print(f"{model_name} parameters.")
      print(
          "{:<20} {:<10} {:<20} {}".format(
              "Parameter", "Required", "Default value", "Description"
          )
      )
      for param in model_params:
          if type(model_params[param]) in [list, tuple]:
              default_value = (
                  "[" + ", ".join(map(str, model_params[param])) + "]"
              )
          else:
              default_value = model_params[param]
  
          print(
              "{:<20} {:<10} {:<20} {}".format(
                  param,
                  "yes" if default_value is None else "no",
                  default_value if default_value is not None else "-",
                  model_params_descr[param]
                  if param in model_params_descr
                  else "",
              )
          )
  
imports: import os
  import re
  from sparknlp.internal import _ResourceHelper_moveFile
  from sparknlp.training._tf_graph_builders.ner_dl.create_graph import create_graph
label_desc: Use image node_id 3 for calling the TFGraphBuilderFactory obj's underlying member method code with example usage: obj.print_model_params(model_name) without return types